“The eyes are the windows to the soul”, is a phrase that helps to illustrate the importance of the appearance of eyes in images to people. In photography, many techniques have been developed for automatic and semi-automatic correction of eye related artifacts caused by electronic flash, LED flash, and other types of scene illumination for use in hand held capture devices and image processing software applications. Conventional techniques typically fail to provide corrections that restore the original eye color, image detail, and iris shape in severe eye artifact conditions from scene illumination techniques. Eye artifacts, especially from flash illumination, can appear in photographs of animals and pets requiring special consideration due to their unique eye structures. In addition, there are other conditions and situations that cause eye related artifacts such as magnification distortions from eyeglasses, lens flare from eyeglass lenses, and physical medical conditions that affect the eye.
U.S. Pat. No. 8,260,082B2, entitled “Pet eye correction,” involves identifying expected pupil-location in a digital image determination is made to check that all pixels in a particular region in which the expected pupil-location resides, are provided with a white or a white color. A target color is computed based on analysis of the pixels in a region in which the location resides. A presumed pupil region is identified. An image of a pupil arranged to fit the pupil region is inserted into the pupil region, where the image of the pupil is an image of an iris, this is a generic image and does not replicate the original pupil color or details. Correcting color defects in a pupil of a human and an animal such as pet cat or dog, represented in a digital image, using a computer system. Uses include but are not limited to desktop computer, laptop computer, mainframe computer, personal digital assistant, Blackberry, smartphone device, digital camera and cellular phone. This enables appropriately scaling and inserting the pupil image into the presumed pupil region to facilitate correction of cue ball condition. The presumed pupil region may be identified based at least upon an analysis of the type of animal or person whose pupil is being corrected, as well as the relative size and shape of the pupil being corrected.
U.S. Pat. No. 7,675,652B2, entitled “Correcting eye color in a digital image,” describes removing an undesired eye color from a digital image utilized in a flash photography device e.g. digital camera, a web-based camera and an electronic communications device camera such as cell phone, blackberry and personal digital assistant.
U.S. Pat. No. 7,035,462B2, entitled “Apparatus and method for processing digital images having eye color defects,” describes graphic user interface and workflow for manual enhancement of automatic red eye correction. The device has a processing unit to detect one or more candidate position of eye color defects in a digital image. A correction unit applies an eye color defect algorithm to the image at the detected candidate positions to correct for the defect. A display presents a portion of the image with corrected eye color defects. An indicator depicts the corrected eye color defects presented on the display.
U.S. Pat. No. 8,559,668B2, entitled “Red-eye reduction using facial detection,” involves calculating a distance between two eyes in an original image using a set of received coordinates. A skin tone sample is obtained from the image based on the calculated distance and the received coordinates. A skin tone color region is generated in a color space based on the obtained skin tone sample. A pixel is classified corresponding to one of the eyes as a red-eye pixel by comparing the pixel with the generated skin tone color region and a predetermined red-eye color region. An indication of the classification relative to the pixel is stored.
U.S. Pat. No. 6,873,743B2, entitled “Method and apparatus for the automatic real-time detection and correction of red-eye defects in batches of digital images or in handheld appliances,” describes a segment including a red-eye defect in a digital image is identified based on red chrominance and luminance of a color map. The segment is eliminated based on testing threshold value by comparing the attributes of the identified segment and its boundary region with a threshold value. The location, size and pixels of the segment that is not eliminated are recorded, to confirm a red-eye defect.